In NLP, language-gender associations are commonly grounded in the author's gender identity, inferred from their language use.However, this identity-based framing risks reinforcing stereotypes and marginalizing individuals who do not conform to normative language-gender associations.To address this, we operationalize the language-gender association as a perceived gender expression of language, focusing on how such expression is externally interpreted by humans, independent of the author's gender identity.We present the first dataset of its kind: 5,100 human annotations of perceived gendered style-human-written texts rated on a five-point scale from very feminine to very masculine.While perception is inherently subjective, our analysis identifies textual features associated with higher agreement among annotators: formal expressions and lower emotional intensity.Moreover, annotator demographics influence their perception: women annotators are more likely to label texts as feminine, and men and non-binary annotators as masculine.Finally, feature analysis reveals that text's perceived gendered style is shaped by both affective and function words, partially overlapping with known patterns of language variation across gender identities.Our findings lay the groundwork for operationalizing gendered style through human annotation, while also highlighting annotators' subjective judgments as meaningful signals to understand perceptionbased concepts.1 n_dependency_npmod has_missing_values n_dependency_root has_missing_values n_tokens high collinearity n_types high collinearity n_characters high collinearity maas_index high collinearity n_hapax_legomena high collinearity n_global_token_hapax_legomena high collinearity n_hapax_dislegomena high collinearity n_global_lemma_hapax_dislegomena high collinearity n_global_token_hapax_dislegomena high collinearity n_syllables high collinearity flesch_reading_ease high collinearity flesch_kincaid_grade high collinearity Feature Reason ari high collinearity cli high collinearity gunning_fog high collinearity lix high collinearity rix high collinearity n_dependency_advmod high collinearity n_dependency_prep high collinearity n_dependency_punct high collinearity raw_sequence_length high collinearity lemma_token_ratio high collinearity n_lemmas high collinearity cttr high collinearity ttr high collinearity herdan_c high collinearity rttr high collinearity mattr high collinearity yule_k high collinearity n_cconj high collinearity n_det high collinearity

Distinguishing LLM-generated text from human-written is a key challenge for safe and ethical NLP, particularly in high-stake settings such as persuasive online discourse.While recent work focuses on detection, real-world use cases also demand interpretable tools to help humans understand and distinguish LLMgenerated texts.To this end, we present an analysis framework comparing human-and LLM-authored arguments using two easilyinterpretable feature sets: general-purpose linguistic features (e.g., lexical richness, syntactic complexity) and domain-specific features related to argument quality (e.g., logical soundness, engagement strategies).Applied to /r/ChangeMyView arguments by humans and three LLMs, our method reveals clear patterns: LLM-generated counter-arguments show lower type-token and lemma-token ratios but higher emotional intensity -particularly in anticipation and trust.They more closely resemble textbook-quality arguments -cogent, justified, explicitly respectful toward others, and positive in tone.Moreover, counter-arguments generated by LLMs converge more closely with the original post's style and quality than those written by humans.Finally, we demonstrate that these differences enable a lightweight, interpretable, and highly effective classifier for detecting LLM-generated comments in CMV.Milad Alshomary and Henning Wachsmuth.2023. Conclusion-based counter-argument generation. In

Research on language as interactive discourse underscores the deliberate use of demographic parameters such as gender, ethnicity, and class to shape social identities. For example, by explicitly disclosing one’s information and enforcing one’s social identity to an online community, the reception by and interaction with the said community is impacted, e.g., strengthening one’s opinions by depicting the speaker as credible through their experience in the subject. Here, we present a first thorough study of the role and effects of self-disclosures on online discourse dynamics, focusing on a pervasive type of self-disclosure: author gender. Concretely, we investigate the contexts and properties of gender self-disclosures and their impact on interaction dynamics in an online persuasive forum, ChangeMyView. Our contribution is twofold. At the level of the target phenomenon, we fill a research gap in the understanding of the impact of these self-disclosures on the discourse by bringing together features related to forum activity (votes, number of comments), linguistic/stylistic features from the literature, and discourse topics. At the level of the contributed resource, we enrich and release a comprehensive dataset that will provide a further impulse for research on the interplay between gender disclosures, community interaction, and persuasion in online discourse.

Warning: This paper contains content that may be offensive or upsetting.Offensive speech is highly prevalent on online platforms.Being trained on online data, Large Language Models (LLMs) display undesirable behaviors, such as generating harmful text or failing to recognize it.Despite these shortcomings, the models are becoming a part of our everyday lives by being used as tools for information search, content creation, writing assistance, and many more.Furthermore, the research explores using LLMs in applications with immense social risk, such as latelife companions and online content moderators.Despite the potential harms from LLMs in such applications, whether LLMs can reliably identify offensive speech and how they behave when they fail are open questions.This work addresses these questions by probing sixteen widely used LLMs and showing that most fail to identify (non-)offensive online language.Our experiments reveal undesirable behavior patterns in the context of offensive speech detection, such as erroneous response generation, over-reliance on profanity, and failure to recognize stereotypes.Our work highlights the need for extensive documentation of model reliability, particularly in terms of the ability to detect offensive language.

We present GRAIN-S, a set of manually created syntactic annotations for radio interviews in German. The dataset extends an existing corpus GRAIN and comes with constituency and dependency trees for six interviews. The rare combination of gold- and silver-standard annotation layers coming from GRAIN with high-quality syntax trees can serve as a useful resource for speech- and text-based research. Moreover, since interviews can be put between carefully prepared speech and spontaneous conversational speech, they cover phenomena not seen in traditional newspaper-based treebanks. Therefore, GRAIN-S can contribute to research into techniques for model adaptation and for building more corpus-independent tools. GRAIN-S follows TIGER, one of the established syntactic treebanks of German. We describe the annotation process and discuss decisions necessary to adapt the original TIGER guidelines to the interviews domain. Next, we give details on the conversion from TIGER-style trees to dependency trees. We provide data statistics and demonstrate differences between the new dataset and existing out-of-domain test sets annotated with TIGER syntactic structures. Finally, we provide baseline parsing results for further comparison.

Classical non-neural dependency parsers put considerable effort on the design of feature functions.Especially, they benefit from information coming from structural features, such as features drawn from neighboring tokens in the dependency tree.In contrast, their BiLSTM-based successors achieve state-ofthe-art performance without explicit information about the structural context.In this paper we aim to answer the question: How much structural context are the BiLSTM representations able to capture implicitly?We show that features drawn from partial subtrees become redundant when the BiLSTMs are used.We provide a deep insight into information flow in transition-and graph-based neural architectures to demonstrate where the implicit information comes from when the parsers make their decisions.Finally, with model ablations we demonstrate that the structural context is not only present in the models, but it significantly influences their performance.

We present a dependency tree linearization model with two novel components: (1) a tree-structured encoder based on bidirectional Tree-LSTM that propagates information first bottom-up then top-down, which allows each token to access information from the entire tree; and (2) a linguistically motivated headfirst decoder that emphasizes the central role of the head and linearizes the subtree by incrementally attaching the dependents on both sides of the head.With the new encoder and decoder, we reach state-of-the-art performance on the Surface Realization Shared Task 2018 dataset, outperforming not only the shared tasks participants, but also previous state-ofthe-art systems (Bohnet et al., 2011;Puduppully et al., 2016).Furthermore, we analyze the power of the tree-structured encoder with a probing task and show that it is able to recognize the topological relation between any pair of tokens in a tree.

We cast sentence boundary detection and syntactic parsing as a joint problem, so an entire text document forms a training instance for transition-based dependency parsing. When trained with an early update or max-violation strategy for inexact search, we observe that only a tiny part of these very long training instances is ever exploited. We demonstrate this effect by extending the ArcStandard transition system with swap for the joint prediction task. When we use an alternative update strategy, our models are considerably better on both tasks and train in substantially less time compared to models trained with early update/max-violation. A comparison between a standard pipeline and our joint model furthermore empirically shows the usefulness of syntactic information on the task of sentence boundary detection.